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Article

A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease

1
Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.4, 11/13 Weston Street, London SE1 3ER, UK
2
Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK
3
Dementia Research Center, Institute of Neurology, University College London, Queen Square, London WC1N 3AR, UK
4
Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s, University of London, Cranmer Terrace, London SW17 0RE, UK
5
Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria, 98165 Messina, Italy
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The Starr Lab, University of California San Francisco, 513 Parnassus Ave, HSE-823, San Francisco, CA 94143, USA
7
Parkinson’s Disease and Movement Disorders Center, Department of Neurology, Parkinson Foundation Center of Excellence, University of South Florida, 4001 E Fletcher Ave, Tampa, FL 33613, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Cosimo Ieracitano, Mufti Mahmud, Maryam Doborjeh and Aime’ Lay-Ekuakille
Sensors 2021, 21(16), 5437; https://doi.org/10.3390/s21165437
Received: 16 July 2021 / Revised: 4 August 2021 / Accepted: 8 August 2021 / Published: 12 August 2021
Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson’s disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson’s disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician—for example, during remote home assessments. Videos (n=729) were collected as part of routine MDS-UPDRS gait assessments of Parkinson’s patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson’s r=0.80, p<0.001). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians’ ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman’s ρ=0.52, p<0.001). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model’s objective UPDRS rating estimation. The severity of gait impairment in Parkinson’s disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring. View Full-Text
Keywords: Parkinson’s disease; gait; time series analysis; computer vision; pose estimation; interpretable machine learning Parkinson’s disease; gait; time series analysis; computer vision; pose estimation; interpretable machine learning
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MDPI and ACS Style

Rupprechter, S.; Morinan, G.; Peng, Y.; Foltynie, T.; Sibley, K.; Weil, R.S.; Leyland, L.-A.; Baig, F.; Morgante, F.; Gilron, R.; Wilt, R.; Starr, P.; Hauser, R.A.; O’Keeffe, J. A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease. Sensors 2021, 21, 5437. https://doi.org/10.3390/s21165437

AMA Style

Rupprechter S, Morinan G, Peng Y, Foltynie T, Sibley K, Weil RS, Leyland L-A, Baig F, Morgante F, Gilron R, Wilt R, Starr P, Hauser RA, O’Keeffe J. A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease. Sensors. 2021; 21(16):5437. https://doi.org/10.3390/s21165437

Chicago/Turabian Style

Rupprechter, Samuel, Gareth Morinan, Yuwei Peng, Thomas Foltynie, Krista Sibley, Rimona S. Weil, Louise-Ann Leyland, Fahd Baig, Francesca Morgante, Ro’ee Gilron, Robert Wilt, Philip Starr, Robert A. Hauser, and Jonathan O’Keeffe. 2021. "A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease" Sensors 21, no. 16: 5437. https://doi.org/10.3390/s21165437

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